Evolution of Cellular Systems - Optimization of Gene Regulatory Networks for the Development of Morphology and Control

Evolution of Cellular Systems - Optimization of Gene Regulatory Networks for the Development of Morphology and Control -
PDF
lschramm-diss_emb.pdfAvailable under Only rights of use according to UrhG.
Download (13MB)
| Preview

Item Type:

Book

Title:

Evolution of Cellular Systems - Optimization of Gene Regulatory Networks for the Development of Morphology and Control

Language:

English

Abstract:

This thesis focuses on the evolution of the development of simulated organisms, where a biologically inspired model to simulate the development of organisms is presented. The individuals are made up of several cells that mechanically interact with each other and can perform different actions, e.g. cell division and cell death. The development of the individual starts with a single cell, whose actions are controlled by a gene regulatory network.

The motivation of this thesis is therefore twofold. One aim is to improve the computational experiments that can in the future shed light on transitions in evolutionary biology which can not be analyzed with the available biological data. The other aim is to use this knowledge to enhance the performance of engineering design, e.g. to optimize topologies.

In the first part of this thesis three different analyzes of morphological development are performed. A stable development that includes self-repairing behavior is evolved and analyzed. Dynamic stability can be achieved without fixing the cells on a grid or using contact inhibition, as most other models do. It is shown that genetic redundancy during the evolution is important and thus removing redundancy during the evolution decreases its performance. A new measurement for the probability of a redundant gene to become functional, the functional proximity, is presented. Genomes with a high functional proximity are shown to improve the evolution.

Most models in literature either optimize only the shape or the structure, or if both are optimized separated genomes are used. Evolving both concurrently and in one genome can result in positive effects on the resulting organisms. Such a model is presented in the second part. The individuals are evolved to fulfill a function that depends on shape and control. First, a spiking neural network is evolved, then a swimming organism is developed with a genome separated to describe the shape and control individually. In the final part, both parts, the development of shape and control are merged into one genome. Extending the concept of concurrent shape and control optimization using one genome to an engineering context may hold the potential for improvements on current practice.